Senior Inference Engineer, GPU Kernel Optimization

Responsibilities

• The role drives three interconnected systems, all aimed at accelerating NVIDIA's LLM inference stack. The first is GPU kernel microbenchmarking: measuring competing kernel implementations at real-silicon fidelity across the full configuration space that production LLM deployments demand. The second is end-to-end model…
• What we need to see
• Master's or PhD in Computer Science, Computer Engineering, or a related field, or equivalent experience.
• 6+ years of relevant industry experience.
• Experience building or directing agentic AI systems — code generation, automated optimization, or multi-step reasoning workflows.
• Strong Python and C++ skills with proven software engineering fundamentals.
• Hands-on GPU profiling with CUPTI, NSYS, and NCU; proven track record to attribute bottlenecks across kernel execution, compiler decisions, and runtime scheduling.
• Direct experience with LLM inference frameworks such as TRT-LLM, SGLang, or vLLM and clear understanding of how kernel selection drives model-level throughput and latency.
• Working knowledge of GPU kernel optimization — CUDA, CUTLASS, Triton, or equivalent — and the ability to read PTX or SASS output.
• Ways to stand out from the crowd

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